We follow [1]} papernot2018deep to retrieve training examples using nearest neighbour in the context representation space. For our model, we use the context representation \(\mathbf {c}\)  defined by Equation REF . For the baseline model, we use the attention-based context representation \(h(c)\)  defined in Equation REF . This can reveal which training instances support the prediction of a model. As shown in Table REF , for the example in Figure REF , the most similar contexts retrieved by our model's context representation are all with preposition “In" ahead of the mention and the golden entities of them are all American cities. In contrast, the baseline's local context is a bag-of-words representation which we denote using the top 10 attended contextual words sorted by attention weights. The most similar contexts retrieved by baseline's context representation share common words like “games", “victory" and the golden entities of them are all baseball teams. This explains why the baseline model incorrectly links the mention “Milwaukee" to Milwaukee_Brewers while our model can link to the correct entity Milwaukee.
